1. Field of the Invention
The present invention relates to a similar image retrieval device, a method of operating a similar image retrieval device, and a non-transitory computer readable recording medium having a similar image retrieval program recorded thereon.
2. Description of the Related Art
Hitherto, similar image retrieval has been performed in which a similar image which is similar to a retrieved image is retrieved from a plurality of instance images. In the similar image retrieval, a feature amount corresponding to a specific pattern which is taken visual notice of by a person within an image is calculated with respect to the retrieved image and the instance image, and a similarity between the retrieved image and the instance image is determined on the basis of the calculated feature amount. The similar image retrieval is put to great practical use in, particularly, the field of medicine. Specifically, the similar image retrieval is used when a doctor makes a specific diagnosis of a disease on the basis of a lesion which is reflected in an inspection image obtained by radiographing a patient using a CT (Computed tomography) device, an MM (Magnetic Resonance Imaging) device, a general X-ray photography device, or the like.
JP2011-118543 discloses a similar image retrieval device that retrieves a similar image to an inspection image used for a diagnosis in a patient equivalent to a retrieved image from a plurality of past case images equivalent to instance images, through the utilization of similar image retrieval, and provides the retrieved similar image to a doctor.
A lesion which is reflected in an inspection image includes ground-glass opacity, an infiltrative shadow, or a plurality of types such as a honeycomb lung, and a lesion which is reflected in one inspection image is not necessarily one type. In case where there are multiple types of lesion, a doctor needs to identify a subtle difference for each lesion. In addition, since the type of lesion changes with the progression of an illness, a certain amount of experience is required for a doctor to make a diagnosis without any help with only radiographic interpretation of an inspection image. Consequently, as in JP2011-118543, by the utilization of the similar image retrieval, the type of lesion of the inspection image can be specified with a hint of the similar image, and even a less-experienced doctor can make a diagnosis having higher accuracy.
The similar image retrieval device disclosed in JP2011-118543 calculates feature amounts from an inspection image which is a retrieved image and a case image which is an instance image, using various types of lesion as patterns. The feature amounts include multiple types such as things related to a pixel value such as an average, dispersion, a maximum value, a minimum value, or a histogram of a pixel value, things related to a shape such as a position or a contour, and things related to a size such as a radius, a volume, or an area.
In similar image retrieval device disclosed in JP2011-118543, as shown in Expression 1 of paragraph <0050>, each difference |Mi−mi| between multiple types of feature amount Mi (i=1, 2, . . . , n) calculated from an inspection image and multiple types of feature amount mi (i=1, 2, . . . , n) calculated from a case image is obtained, and a sum (Σwi|Mi−mi|) obtained by multiplying each difference by an appropriate weighting coefficient wi (i=1, 2, . . . , n) is calculated as a similarity between the inspection image and the case image.
The similarity calculated in this manner indicates a distance in an n-dimensional vector space of two n-dimensional vectors (often called feature vectors) using each of the feature amounts Mi and mi as an element. It is determined that as the difference between the feature amounts Mi and mi decreases (as a distance between two n-dimensional vectors using each of the feature amounts Mi and mi as an element becomes shorter), the similarity becomes smaller, and that the inspection image and the case image have a high similarity therebetween. On the other hand, it is determined that as the difference between the feature amounts Mi and mi increases (as a distance between two n-dimensional vectors using each of the feature amounts Mi and mi as an element becomes longer), the similarity becomes larger, and that the inspection image and the case image have a low similarity therebetween.
As in the similar image retrieval device disclosed in JP2011-118543, when the similarity between the inspection image and the case image is calculated on the basis of the feature amounts, the reliability of the similarity is dependent on the calculation accuracy of the feature amounts. In addition, since the feature amount is a predetermined numerical value, and is not a value obtained by completely reproducing a human appearance, an estrangement between a human appearance and the similarity calculated on the basis of the feature amounts may occur. Particularly, when the feature amount has multiple types, the calculation accuracy differs depending on the types, and the degree of estrangement from a human appearance also differs depending on the types. For this reason, there is a greater tendency for the reliability of the similarity to be influenced.
Further, as in the similar image retrieval device disclosed in JP2011-118543, during the calculation of the similarity between the inspection image and the case image on the basis of the feature amounts, similarities are the same as each other in case where the feature amounts of the inspection image and the case image are substantially coincident with each other in both large values, and in case where the feature amounts of the inspection image and the case image are substantially coincident with each in both low values.
Generally, a feature amount corresponding to a certain pattern shows that as the value becomes larger, the existence probability of the pattern within an image increases. Therefore, from an object of similar image retrieval to obtain a similar image to an inspection image, a similarity in case where the feature amounts of the inspection image and the case image are substantially coincident with each other in both large values has to be made higher than in case where the feature amounts of the inspection image and the case image are substantially coincident with each other in both low values. However, in JP2011-118543, this point is not considered. Therefore, it is not necessarily possible to retrieve a similar image having a sense of consent.